Pandas：根据索引值插入列

I have the following example data

我有以下示例数据

In [1]: table[['id', 'age']]
Out[1]: 
   id  age
0   1   12
1   2   13
2   3   14
3   4   15
4   5   16
5   6   17
6   7   18
7   8  NaN
8   9   20

ad the following np.array

广告以下np.array

In [2]: data
Out[2]: 
array([[  1.,   3.,  21.],
       [  2.,   4.,  21.],
       [  3.,   5.,  22.],
       [  4.,   5.,  22.],
       [  5.,   4.,   2.]])

and would like to concatenate the data to the table according to the id.

并希望根据id将数据连接到表。

For example the result should look like:

例如,结果应如下所示:

id  age
0   1   12  3     21
1   2   13  4     21 
2   3   14  5     22
3   4   15  5     22
4   5   16  4     2
5   6   17  
6   7   18  
7   8  NaN  
8   9   20  

I can loop over zip(table,data) and work line by line, but I thought it could be done in a more concise way

我可以循环拉链(表格,数据)并逐行工作,但我认为可以用更简洁的方式完成

1 个解决方案

#1

I'm posting this as an answer according to your text desired result rather than your posted result df, I can edit it easily if that's what you really want.

我根据您的文本期望结果发布此作为答案,而不是您的发布结果df,如果这是您真正想要的,我可以轻松编辑它。

Firstly construct a df from the np array and then merge this. We merge the left side on 'id' and the right side on column '0' and perform an outer merge, we have to drop the '0' column as it's superfluous to what we want:

首先从np数组构造一个df然后合并它。我们将“id”左侧和“0”列右侧合并并执行外部合并,我们必须删除“0”列,因为它对我们想要的东西来说是多余的:

In [261]:

data = np.array([[  1.,   3.,  21.],
       [  2.,   4.,  21.],
       [  3.,   5.,  22.],
       [  4.,   5.,  22.],
       [  5.,   4.,   2.]])
data
Out[261]:
array([[  1.,   3.,  21.],
       [  2.,   4.,  21.],
       [  3.,   5.,  22.],
       [  4.,   5.,  22.],
       [  5.,   4.,   2.]])
In [265]:

data_df = pd.DataFrame(data)
data_df
Out[265]:
   0  1   2
0  1  3  21
1  2  4  21
2  3  5  22
3  4  5  22
4  5  4   2
In [268]:

df.merge(data_df, left_on='id', right_on=0, how='outer').drop(0, axis=1)
Out[268]:
   id  age   1   2
0   1   12   3  21
1   2   13   4  21
2   3   14   5  22
3   4   15   5  22
4   5   16   4   2
5   6   17 NaN NaN
6   7   18 NaN NaN
7   8  NaN NaN NaN
8   9   20 NaN NaN